A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers

TL;DR

This paper introduces a sparse learning framework for detecting multiple noise-like jammers in radar systems, utilizing likelihood ratio tests and cyclic estimation to improve detection and estimation accuracy.

Contribution

It proposes two novel architectures that jointly detect unknown numbers of NLJs and estimate their angles of arrival using sparsity-promoting priors, addressing intractability in classical methods.

Findings

Effective detection of multiple NLJs demonstrated on simulated data

Improved estimation accuracy of jammer angles

Framework exploits the sparse nature of the problem

Abstract

In this paper, we address the problem of detecting multiple Noise-Like Jammers (NLJs) through a radar system equipped with an array of sensors. To this end, we develop an elegant and systematic framework wherein two architectures are devised to jointly detect an unknown number of NLJs and to estimate their respective angles of arrival. The followed approach relies on the likelihood ratio test in conjunction with a cyclic estimation procedure which incorporates at the design stage a sparsity promoting prior. As a matter of fact, the problem at hand owns an inherent sparse nature which is suitably exploited. This methodological choice is dictated by the fact that, from a mathematical point of view, classical maximum likelihood approach leads to intractable optimization problems (at least to the best of authors' knowledge) and, hence, a suboptimum approach represents a viable means to solve them. Performance analysis is conducted on simulated data and shows the effectiveness of the proposed architectures in drawing a reliable picture of the electromagnetic threats illuminating the radar system.